Computer-aided detection (CAD) generally refers to the use of computers to analyze medical images to detect anatomical abnormalities therein. Sometimes used interchangeably with the term computer-aided detection are the terms computer-aided diagnosis, computer-assisted diagnosis, or computer-assisted detection. As used herein, CAD detection refers to a location in a medical image that a CAD system, in accordance with a CAD algorithm operating on the medical image, has identified as warranting some type of attention by a radiologist. As used herein, radiologist generically refers to a medical professional that analyzes medical images and makes clinical determinations therefrom, it being understood that such person might be titled differently, or might have differing qualifications, depending on the country or locality of their particular medical environment.
As known in the art, a CAD algorithm usually identifies a preliminary set of candidate detections in a medical image and then selects which ones, if any, will qualify as actual CAD detections based on a variety of computed features associated with the candidate detections. The CAD results, i.e., the body of information associated with the operation of the CAD algorithm on the medical image, are most often communicated in the form of annotation maps comprising graphical annotations (CAD markers) overlaid on a diagnostic-quality or reduced-resolution version of the medical image, one CAD marker for each CAD detection.
CAD results are mainly used by radiologists as “secondary reads” or secondary diagnosis tools. When analyzing a medical image, the radiologist usually makes his or her own analytical determinations before looking at the CAD results, which either verify those determinations or trigger further inspection of the image. Some CAD implementations have used CAD results in a “concurrent reading” context in which the radiologists look at the CAD results at the same time that they look at the images.
In the field of x-ray mammography, thousands of mammography CAD systems are now installed worldwide, and are used to assist radiologists in the interpretation of millions of mammograms per year. Mammography CAD systems are described, for example, in U.S. Pat. No. 5,729,620, U.S. Pat. No. 5,815,591, U.S. Pat. No. 5,917,929, U.S. Pat. No. 6,075,879, U.S. Pat. No. 6,266,435, U.S. Pat. No. 6,434,262, and U.S. 2001/0043729A1, each of which is incorporated by reference herein. Mammography CAD algorithms analyze digital or digitized images of standard mammographic views (e.g. CC, MLO) for characteristics commonly associated with breast cancer, such as calcifications, masses, and architectural distortions. It is to be appreciated that although presented in the particular context of x-ray mammography, the preferred embodiments described herein are applicable for a variety of medical imaging modalities such as computerized tomography (CT) imaging, magnetic resonance imaging (MRI), positron emission tomography (PET), single-photon emission computed tomography (SPECT), and ultrasound, and even less conventional medical imaging modalities such as thermography, electrical conductivity-based modalities, and the like.
Substantial effort and attention has been directed to increasing the analysis capabilities of CAD systems, resulting in ever-increasing amounts of information that is available to the radiologist for review. By way of example, U.S. Pat. No. 6,434,262, supra, describes providing enhanced image tiles for each CAD detection including close-up viewing capabilities. In another example, U.S. Pat. No. 6,266,435, supra, describes providing numerical characterizations adjacent to each CAD detection such as probabilities or other predictive values, describes analog representations of such characterizations within the CAD markers themselves (e.g., shape, size, color), and describes displaying different sets of CAD markers for different thresholds that may even be user-selectable. By way of still example, U.S. 2001/0043729A1, supra, discusses a search workstation in which the display shows both a computer classification output for a lesion as well as images of lesions from other medical images with known diagnoses based on a similarity of computer-extracted features.
Problems can arise, however, at the interface between (a) the amount of information available to the radiologist, and (b) the amount of information that can be usefully perceived by the radiologist in a reasonable amount of time. These issues are especially important in today's radiology environment, where there is a ongoing tension between providing high-quality detection/diagnosis for each patient and maintaining adequate patient throughput to keep costs under control. A large body of information associated with a medical image would have substantially diminished value if the radiologist does not have the time or inclination to view that information. In an almost-humorous reflection of this situation, U.S. 2004/0223633A1 discusses automatically inserting false, misleading CAD markers in medical images to ensure that the radiologist, knowing that one of the marks might be false, will perform a more thorough review. Other issues arise as would be apparent to one skilled in the art upon reading the present disclosure.
Accordingly, it would be desirable to provide a medical review workstation that provides a judicious selection of helpful information to the radiologist for assisting in the screening and/or diagnosis of a medical image.
It would be further desirable to facilitate quick yet contextually meaningful navigation among multiple CAD detections in a medical image.
It would be still further desirable to provide, in the context of multiple CAD detections for a medical image, a tool for quickly perceiving characteristics of those CAD detections relative to each other.
It would be even further desirable to provide such tools in a manner that seamlessly integrates into existing radiology workflows.